Improving Operations with AI and Machine Learning Algorithms

Companies are leveraging AI, automation and data to deliver personalised experiences to customers and potential customers.

Through, leveraging data and information about their interactions with the intention of, creating goal-based workflows. To enable consumers to discover answers to issues that are trying to address expeditiously.

This necessitates a deep understanding of user motivations and the building of actionable data as a feature. Essentially this means embedding analytics into applications. To enable the emergence of intuitive visualisations, that customers can understand.

Contextual interactions between customer experience teams and potential customers. And as always, the extraction of actionable data, to elevate the user experience.

Artificial Intelligence can be Divided into 3 Parts

Machine learning is primarily concerned with the construction of algorithms, embedded with data that will enable business managers and data scientists to uncover actionable insights. Data is split into two components one for testing the algorithm and one set of data for training the algorithm.

There are three forms of machine learning, supervised and unsupervised the objective of supervised learning is to train the model to take a given data point and map it to the desired output value.

And reinforcement learning which is the training of machine learning models to make a sequence of decisions. To achieve a goal in an uncertain and complex environment.

Types of Machine Learning

Supervised Learning: The algorithm is trained with data that has already been correctly labelled. This alludes to the cleaning of data which consumes the majority of data scientist’s time. Examples of supervised learning is sentiment analysis used to predict a tweet or review from customers

Unsupervised Learning: In unsupervised learning, the data contains no labels, is referred to as unlabelled data, the objective here is to allow the algorithm over time to cluster data into groups of similar data without naming the data. Examples of use cases for unsupervised learning includes;

Customer Segmentation: to understand different groups as the foundation for the development of marketing and business strategies.

Genetics: through clustering DNA, to analyse evolutionally biology. Recommender systems deployed to group users with similar viewing patterns to recommend similar content.

Anomaly Detection: to identity fraud, or for predictive maintenance.

Reinforcement Learning: Reinforcement learning is the training of machine learning models to make a sequence of decisions. To achieve a goal in an uncertain and complex environment. The three methods of reinforcement learning are; Value based, Policy based and Mobile based learning. Applications of reinforcement learning included self-driving cars, natural language processing for text summarisation, question answering and machine translation.

Semi-supervised Learning: is a hybrid of supervised and unsupervised learning. Using the techniques of both supervised and unsupervised learning. Over the past couple of years, AI and ML have played an increasingly pivotal role in the management and forecasting of inventory demand accurately.

Machine learning and data analysis enable retailers to obtain insights into sales information and trends from various sources such as social media, retail sales and blog posts.

The application of Machine Learning in e-commerce includes: sentiment analysis, wallet share estimations, fraud detection, query expansion, churn prediction, inventory management, channel optimisation, market basket analysis and so on.

Machine learning techniques such as neural network, recurrent neural network and support vector machine (SVM) play a major role in demand forecasting.

Machine learning practitioners leverage, cloud computing, big data and the learning capabilities of machine learning algorithms to outperform traditional regression models.

Data is the new oil and retailers need to exploit the insights gained from the analysis of this information.

The opportunity to harness the power of advanced analytics; big data, pricing algorithms and machine learning. May potentially lead to increases in gross margins of between 10% to 20 %. However, for data to be useful, it starts with;

The collection, cleaning, integration and matching of structured transactional and unstructured non-transactional data. Fragmented across departments, apps, websites and third-party organisations. This is paramount to bring an accurate verifiable picture of consumers, new markets, and product opportunities into focus.

Unstructured data in particular is neglected by organisations. However, data in general is growing significantly every year. According to IDC the total amount of data both structure and unstructured that will be produced by 2024 is 163 zettabytes

Units of Data – Digital Storage or Memory

As a result of the increase in the size of the volume of transactions, conducted online due in part to Covid-19.

The increase in online browsing data, social media data, mobile usage data, and customer satisfaction data. Organisations are using statistical, econometric and data science models developed for enabling appropriate data -driven decisions.

In parallel, the constant collection of data on consumer transactions and behaviours on platforms will require the automation of decisions in real-time.

Statistics on Consumers Migrating Online

Download the accompanying report the Ascent of the Digital Economy

Machine Learning Algorithms and Assisted Decision Systems

Machine learning algorithms are the backbone of the generation of AI-assisted decisions systems such as chatbots and robots.

Chatbots are natural language interfaces deployed for customer support, booking tickets to events or shows within chatbots these are apps within apps. Chatbots can find products and check inventory and can enable a remarkable customer experience.

The word chatbot is derived from “chat robots” which operate as machine agents that serve as natural language user interfaces to data and services through text or voice. The software mimics a conversation you ask it a question and it will provide a suitable answer.

Natural language interfaces enable users i.e., customers or employees to interact using spoken words from AI assistants such as Siri, Alexa or Cortana and Google Now are or written command such as; Google Search, Bing, and Firefox.

Chatbots - User Motivation

Chatbots for Productivity: The vast majority of participants (68%) reported productivity to be the main reason for using chatbots. These participants highlighted the ease, speed, and convenience of using chatbots.

Also, they noted that chatbots provide assistance and access to information. With the ease of use, speed, and convenience as their main reasons for using chatbots.

Chatbots for Productivity

The vast majority of users that is 64% reported productivity to be the main reason for using chatbots. These participants highlighted the ease, speed, and convenience of using chatbots. Also, they noted that chatbots provide assistance and access to information.

Ease, speed, and convenience Forty-two percent of participants reported ease of use, speed, and convenience as their main reasons for using chatbots.

Chatbots for Entertainment

Some consumers use chatbots as a form of entertainment value with consumers perceiving chatbots to be fun and entertaining.

Social or relational

Typically, chatbots are seen as a personal, human means of interaction that may have social value. Some also use chatbots to strengthen social interactions with other people

Top User Benefits According to Customers - Digital Experience

To deliver exemplary user experiences within the confines a chatbot requires not viewing data as just a by-product of the apps, but a critical feature that enables organisations to identify the goals of visitors to their platforms, apps and websites.

What are users’ goals, and how do you leverage chatbot data in a way that helps them take action, make decisions and achieve their goals?

A key success factor for chatbots and natural language user interfaces is how well they can support user needs in the conversational process seamlessly and efficiently.

These applications increase individual flexibility, expand opportunities for information retrieval and learning.

However, chatbots are a long way from being conversational, currently, the primary goal is to answer a customer’s question as quickly as possible.